# Load dataset
df = pd.read_csv("data/security_incidents_cleaned.csv")
# Countries of interest
countries = [
"Occupied Palestinian Territories", "Afghanistan", "DR Congo",
"Syrian Arab Republic", "Somalia", "Mali", "South Sudan", "Sudan"
]
# Filter data for those countries
df_filtered = df[df['country'].isin(countries)]
# Calculate total incidents per country for normalization
country_totals = df_filtered.groupby('country').size().to_dict()# Your visualization code here
# Columns to analyze
columns_to_check = [
"means_of_attack", "attack_context", "location",
"motive", "actor_type", "actor_name"
]
# Calculate total incidents per country for normalization
country_totals = df_filtered.groupby('country').size()
# Initialize a DataFrame to store results - with explicit float dtype
results = pd.DataFrame(index=countries, columns=columns_to_check, dtype=float)
# Count "Unknown" values in each column for each country
value = "Unknown" # Change this to analyze a different value
for country in countries:
country_data = df_filtered[df_filtered['country'] == country]
country_total = len(country_data)
if country_total > 0:
for col in columns_to_check:
# Count occurrences and calculate percentage
count = country_data[country_data[col] == value].shape[0]
percentage = (count / country_total) * 100
results.loc[country, col] = percentage
else:
for col in columns_to_check:
results.loc[country, col] = 0.0
# Calculate the average percentage across all columns
results['average'] = results[columns_to_check].mean(axis=1)
# Sort by average (descending)
results = results.sort_values('average', ascending=False)
# Create the bar chart
plt.figure(figsize=(14, 8))
# Plot grouped bars for each column
results[columns_to_check].plot(
kind='bar',
ax=plt.gca(),
width=0.8
)
# Add a line for the average
plt.plot(
range(len(results)),
results['average'],
'ko-',
linewidth=2,
markersize=8,
label='Average'
)
# Calculate total counts of Unknown values
unknown_counts = {}
for country in countries:
country_data = df_filtered[df_filtered['country'] == country]
unknown_total = country_data[columns_to_check].apply(
lambda row: (row == value).sum(), axis=1
).sum()
unknown_counts[country] = unknown_total
# Add annotations for averages
for i, (country, avg) in enumerate(results['average'].items()):
plt.annotate(
f"{avg:.1f}%\n({unknown_counts[country]} occurrences)",
xy=(i, avg),
xytext=(0, 10),
textcoords='offset points',
ha='center',
va='bottom',
fontweight='bold',
bbox=dict(boxstyle="round,pad=0.3", fc="white", ec="gray", alpha=0.8)
)
plt.title(f"Percentage of '{value}' Values Across All Columns by Country")
plt.xlabel("Country")
plt.ylabel(f"Percentage of '{value}' Values")
plt.legend(title="Column", bbox_to_anchor=(1.01, 1), loc='upper left')
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()
# Columns to analyze
columns_to_check = [
"means_of_attack", "attack_context", "location",
"motive", "actor_type", "actor_name"
]
# Calculate total incidents per country for normalization
country_totals = df_filtered.groupby('country').size()
# Initialize a DataFrame to store results - with explicit float dtype
heatmap_data = pd.DataFrame(index=countries, columns=columns_to_check, dtype=float)
# Count "Unknown" values in each column for each country
value = "Unknown" # Change this to analyze a different value
for country in countries:
country_data = df_filtered[df_filtered['country'] == country]
country_total = len(country_data)
if country_total > 0:
for col in columns_to_check:
# Count occurrences and calculate percentage
count = country_data[country_data[col] == value].shape[0]
percentage = (count / country_total) * 100
heatmap_data.loc[country, col] = percentage
else:
for col in columns_to_check:
heatmap_data.loc[country, col] = 0.0
# Calculate average for sorting
heatmap_data['average'] = heatmap_data[columns_to_check].mean(axis=1)
heatmap_data = heatmap_data.sort_values('average', ascending=False)
# Create heatmap visualization (without the average column)
plt.figure(figsize=(14, 8))
sns.heatmap(
heatmap_data[columns_to_check],
annot=True,
fmt=".1f",
cmap="YlOrRd",
linewidths=0.5,
cbar_kws={'label': '% Unknown'}
)
plt.title("Percentage of 'Unknown' Values by Country and Column")
plt.tight_layout()
plt.show()
# Columns to analyze
columns_to_check = [
"means_of_attack", "attack_context", "location",
"motive", "actor_type", "actor_name"
]
# Count 'Unknown' values per row
df_filtered["unknown_count"] = df_filtered[columns_to_check].apply(
lambda row: (row == "Unknown").sum(), axis=1
)
# Calculate total unknown values per country
country_unknown = df_filtered.groupby('country')['unknown_count'].sum()
# Calculate total possible unknown values (# rows * # columns)
country_total_possible = df_filtered.groupby('country').size() * len(columns_to_check)
# Calculate percentages
unknown_pct = (country_unknown / country_total_possible * 100).reindex(countries)
known_pct = 100 - unknown_pct
# Sort by percentage of known values (ascending)
sorted_countries = known_pct.sort_values().index
# Create DataFrame for plotting
stacked_data = pd.DataFrame({
'country': sorted_countries,
'Known': known_pct[sorted_countries].values,
'Unknown': unknown_pct[sorted_countries].values
})
# Reshape for plotting
plot_data = pd.melt(
stacked_data,
id_vars=['country'],
value_vars=['Known', 'Unknown'],
var_name='Data Status',
value_name='Percentage'
)
# Plot
plt.figure(figsize=(12, 7))
ax = sns.barplot(
data=plot_data,
x='Percentage',
y='country',
hue='Data Status',
palette=['#1D70B8', '#F2645A'] # Blue for Known, Red for Unknown
)
# Add percentage labels
for i, country in enumerate(sorted_countries):
known = known_pct[country]
plt.text(
50,
i,
f"{known:.1f}% Complete",
ha='center',
va='center',
color='white',
fontweight='bold'
)
plt.title("Data Completeness by Country (%)")
plt.xlabel("Percentage")
plt.ylabel("Country")
plt.legend(title='', loc='lower right')
plt.tight_layout()
plt.show()
# Columns to analyze
columns_to_check = [
"means_of_attack", "attack_context", "location",
"motive", "actor_type", "actor_name"
]
# Count 'Unknown' values per row if not already done
if "unknown_count" not in df_filtered.columns:
df_filtered["unknown_count"] = df_filtered[columns_to_check].apply(
lambda row: (row == "Unknown").sum(), axis=1
)
# Get the distribution of records with each number of unknown fields (as percentages)
unknown_dist = pd.crosstab(
index=df_filtered['country'],
columns=df_filtered['unknown_count'],
normalize='index'
) * 100
# Sort countries by average number of unknown fields
avg_unknown = df_filtered.groupby('country')['unknown_count'].mean().sort_values(ascending=False)
unknown_dist = unknown_dist.reindex(avg_unknown.index)
# Plot stacked bars
plt.figure(figsize=(14, 8))
unknown_dist.plot(
kind='barh',
stacked=True,
figsize=(14, 8),
cmap='YlOrRd',
width=0.8
)
# Add text for average unknown count
for i, country in enumerate(unknown_dist.index):
avg = avg_unknown[country]
plt.text(
101,
i,
f"Avg: {avg:.1f} fields",
va='center',
fontsize=10,
fontweight='bold'
)
plt.title("Distribution of Records by Number of Unknown Fields")
plt.xlabel("Percentage of Records")
plt.ylabel("Country")
plt.legend(
title='Number of Unknown Fields',
bbox_to_anchor=(1.01, 1),
loc='upper left'
)
plt.xlim(0, 120) # Leave room for annotations
plt.grid(False)
plt.tight_layout()
plt.show()<Figure size 4200x2400 with 0 Axes>
# Columns to analyze
columns_to_check = [
"means_of_attack", "attack_context", "location",
"motive", "actor_type", "actor_name"
]
# Count 'Unknown' values per row if not already done
if "unknown_count" not in df_filtered.columns:
df_filtered["unknown_count"] = df_filtered[columns_to_check].apply(
lambda row: (row == "Unknown").sum(), axis=1
)
# Calculate total and percentage of unknown values by country
country_stats = []
for country in countries:
country_data = df_filtered[df_filtered['country'] == country]
total_incidents = len(country_data)
if total_incidents > 0:
total_unknown = country_data['unknown_count'].sum()
total_possible = total_incidents * len(columns_to_check)
pct_unknown = (total_unknown / total_possible) * 100
country_stats.append({
'country': country,
'total_incidents': total_incidents,
'total_unknown': total_unknown,
'percent_unknown': pct_unknown,
'avg_unknown_per_record': total_unknown / total_incidents
})
# Convert to DataFrame and sort
stats_df = pd.DataFrame(country_stats)
stats_df = stats_df.sort_values('percent_unknown', ascending=False)
# Create bar chart
plt.figure(figsize=(12, 7))
bars = plt.barh(
stats_df['country'],
stats_df['percent_unknown'],
color='#F2645A',
alpha=0.8
)
# Add text labels
for i, row in enumerate(stats_df.itertuples()):
plt.text(
row.percent_unknown + 0.5,
i,
f"{row.percent_unknown:.1f}% ({row.total_unknown}/{row.total_incidents*len(columns_to_check)})",
va='center'
)
plt.title("Overall Percentage of Unknown Values by Country")
plt.xlabel("Percentage of Unknown Values")
plt.ylabel("Country")
plt.grid(axis='x', alpha=0.3)
plt.tight_layout()
plt.show()